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Abstract:

Embodiments of cognitive radio technology can recover and utilize
under-utilized portions of statically-allocated radio-frequency spectrum.
A plurality of sensing methods can be employed. Transmission power
control can be responsive to adjacent channel measurements. Digital
pre-distortion techniques can enhance performance. Embodiments of a high
dynamic range transceiver architecture can be employed.

Claims:

1: A method for sensing for cognitive radio comprising the steps of:
receiving a first specified signal; performing one or more first
specified sensing functions, wherein the one or more first specified
sensing functions are at least partially responsive to the first
specified signal; performing one or more second specified sensing
functions, wherein the one or more second specified sensing functions are
at least partially responsive to the first specified signal; and
determining a first sensing result, wherein the first sensing result is
at least partially responsive to the one or more first specified sensing
functions and at least partially responsive to the one or more second
specified sensing functions.

2: A system for sensing for cognitive radio comprising: a first cognitive
radio unit comprising: a first sensing element, wherein the first sensing
element provides one or more first specified sensing functions, and,
wherein the one or more first specified sensing functions are at least
partially responsive to a first specified signal; and a second sensing
element, wherein the second sensing element provides one or more second
specified sensing functions, and, wherein the one or more second
specified sensing functions are at least partially responsive to the
first specified signal; and, wherein the first cognitive radio unit
provides a first sensing result at least partially responsive to the one
or more first specified sensing functions and at least partially
responsive to the one or more second specified sensing functions.

Description:

PRIORITY

[0001] This application is related to and claims priority under 35 U.S.C.
119(e) to U.S. Provisional Patent Application No. 60/890,801 filed on
Feb. 20, 2007 entitled "SYSTEM AND METHOD FOR COGNITIVE RADIO" by Haiyun
Tang the complete content of which is hereby incorporated by reference.

BACKGROUND

[0003] 1. Field of the Invention

[0004] The inventions herein described relate to systems and methods for
cognitive radio.

[0005] 2. Description of the Related Art

Spectrum Utilization Problems

[0006] A recent study by the FCC Spectrum Task Force [United States'
Federal Communications Commission (FCC), "Report of the spectrum
efficiency working group," November 2002 found that while the available
spectrum becomes increasingly scarce, the assigned spectrum is
significantly underutilized. This imbalance between spectrum scarcity and
spectrum underutilization is especially inappropriate in this Information
Age, when a significant amount of spectrum is needed to provide
ubiquitous wireless broadband connectivity, which is increasingly
becoming an indispensable part of everyday life.

[0007] Static spectrum allocation over time can also result in spectrum
fragmentation. With lack of an overall plan, spectrum allocations in the
US and other countries over the past several decades can appear to be
random. Despite some efforts to serve best interests at the time, this
leads to significant spectrum fragmentation over time. The problem is
exacerbated at a global level due to a lack of coordinated regional
spectrum assignments. In order to operate under such spectrum conditions,
a device can benefit from operational flexibility in frequency and/or
band shape; such properties can help to maximally exploit local spectrum
availability.

[0008] To address the above problems, an improved radio technology is
needed that is capable of dynamically sensing and locating unused
spectrum segments, and, communicating using these spectrum segments while
essentially not causing harmful interference to designated users of the
spectrum. Such a radio is generally referred to as a cognitive radio,
although strictly speaking, it may perform only spectrum cognition
functions and therefore can be a subtype of a broad-sense cognitive radio
[J. M. III, "Cognitive radio for flexible mobile multimedia
communications," Mobile Networks and Applications, vol. 6, September
2001.] that learns and reacts to its operating environment. Key aspects
of a cognitive radio can include: Sensing: a capability to identify used
and/or unused segments of spectrum. Flexibility: a capability to change
operating frequency and/or band shape; this can be employed to fit into
unused spectrum segments. Non-interference: a capability to avoid causing
harmful interference to designated users of the spectrum.

[0009] Such a cognitive radio technology can improve spectrum efficiency
by dynamically exploiting underutilized spectrum, and, can operate at any
geographic region without prior knowledge about local spectrum
assignments. It has been an active research area recently.

FCC Spectrum Reform Initiatives

[0010] FCC has been at the forefront of promoting new spectrum sharing
technologies. In April 2002, the FCC issued an amendment to Part 15 rules
that allows ultra-wideband (UWB) underlay in the existing spectrum [FCC,
"FCC first report and order: Revision of part 15 of the commission's
rules regarding ultra-wideband transmission systems," ET Docket No.
98-153, April 2002]. In June 2002, the FCC established a Spectrum Policy
Task Force (SPTF) whose study on the current spectrum usage concluded
that "many portions of the radio spectrum are not in use for significant
periods of time, and that spectrum use of these `white spaces` (both
temporal and geographic) can be increased significantly". SPTF
recommended policy changes to facilitate "opportunistic or dynamic use of
existing bands." In December 2003, FCC issued the notice of proposed rule
making on "Facilitating Opportunities for Flexible, Efficient and
Reliable Spectrum Use Employing Cognitive Radio Technologies" [FCC,
"Facilitating opportunities for flexible, efficient, and reliable
spectrum use employing cognitive radio technologies," ET Docket No.
03-108, December 2003] stating that "by initiating this proceeding, we
recognize the importance of new cognitive radio technologies, which are
likely to become more prevalent over the next few years and which hold
tremendous promise in helping to facilitate more effective and efficient
access to spectrum."

[0011] While both UWB and cognitive radio are considered as spectrum
sharing technologies, their approaches to spectrum sharing are
substantially different. UWB is an underlay (below noise floor) spectrum
sharing technology, while cognitive radio is an overlay (above noise
floor) and interlay (between primary user signals) spectrum sharing
technology as shown in FIG. 1. Through sensing combined with operational
flexibility, a cognitive radio can identify and make use of spectral
"white spaces" between primary user signals. Because a cognitive user
signal resides in such "white spaces", high signal transmission power can
be permitted as long as signal power leakage into primary user bands does
not embody harmful interference.

Broadcast TV Bands

[0012] Exemplary broadcast TV bands are shown in Graph 200 of FIG. 2. Each
TV channel is 6 MHz wide. Between 0 and 800 MHz, there are a total of 67
TV channels (Channels 2 to 69 excluding Channel 37 which is reserved for
radio astronomy). The NPRM [FCC, May 2004, op. cit.] excludes certain
channels for unlicensed use: Channels 2-4, which are used by TV
peripheral devices, and Channels 52-69, which are considered for future
auction. Among the channels remaining, Channels 5-6, 7-13, 21-36, and
38-51 are available for unlicensed use in all areas. Unlicensed use in
Channels 14-20 is allowed only in areas where they are not used by public
safety agencies [FCC, May 2004, op. cit.].

[0013] It can be appreciated that Channels 52-69 are currently used by TV
broadcasters and it is not clear if/when they will be vacated. There is
significant interference in the lower channels 5-6 and 7-13. Based on
these considerations, the spectrum segment 470-806 MHz covering TV
channels 14-69 can be of particular interest.

Spectrum Opportunity in the TV Bands

[0014] Spectrum opportunity can be a direct result of incumbent system
inefficiency. In TV bands, a signal from a TV tower can cover an area
with a radius of tens of kilometers. TV receivers can be sensitive to
interference such that TV cell planning may be very conservative to
ensure there is essentially no co-channel interference. This can leave a
substantial amount of "white spaces" between co-channel TV cells as
illustrated in the Map 300 of FIG. 3. Those "white spaces" can constitute
an opportunistic region for cognitive users on a particular TV channel.
Each TV channel may have a differently shaped opportunistic region. The
total spectrum opportunity at any location can comprise the total number
of opportunistic regions covering the location. A measurement in one
locality shows an average spectrum opportunity in TV channels 14-69 of
about 28 channels; that can be expressed as an equivalent bandwidth of
approximately 170 MHz.

[0036]FIG. 4 depicts an embodiment of a cognitive radio system in block
diagram. A transceiver 401 can be coupled to and/or in communication with
one or more antennae 402. Baseband signal processing can be provided by
elements of a baseband processor 403. Elements of a baseband processor
403 can comprise a sensing processor 404, a transmit power control
element 405, and a pre-distortion element 406. In some embodiments a
pre-distortion element 406 can be coupled to and/or in communication with
a transceiver 401. In some embodiments a transmit power control element
can be coupled to and/or in communication with a transceiver 401. In some
embodiments a collective sensing element 407 can be coupled to and/or in
communication with a baseband processor 403 and/or elements comprising a
baseband processor.

[0037] In some embodiments transceiver 401 can comprise transceiver and/or
transmitter and/or receiver mechanisms disclosed herein. In some
embodiments sensing element 404 can comprise one or more sensing
mechanisms as described herein. By way of example and not limitation
these sensing mechanisms can include energy sensing, National Television
Systems Committee (NTSC) signal sensing, and/or Advanced Television
Systems Committee (ATSC) signal sensing. In some embodiments a collective
sensing element 407 can provide collective sensing mechanisms as
described herein.

[0038] In some embodiments transmit power control 405 can support adaptive
transmit power control mechanisms described herein. In some embodiments
pre-distortion element 406 can provide digital pre-distortion mechanisms
as described herein.

[0039] In some embodiments baseband processor 403 can support additional
processing mechanisms as described herein. By way of example and not
limitation these mechanisms can include filtering and/or digital mixing.

Sensing for TV Band Adaptive Spectrum System

[0040] Sensing methods can generally be separated into two categories: 1)
energy sensing and 2) phase sensing. Energy sensing can measure spectrum
energy of a target signal and can be a fundamental sensing method when
nothing else is known about the target signal. In some embodiments, a
received signal can be transformed to a frequency domain in order to
perform an energy sensing operation. Some embodiments can employ a fast
Fourier transform (FFT) and/or any other known and/or convenient
transformation to a frequency domain. Signal energies in specified
frequency ranges can subsequently be measured.

[0041] In some embodiments and/or circumstances phase sensing can achieve
better performance than energy sensing. Phase sensing can require that a
target signal contains one or more known signal patterns. Phase sensing
can be achieved by correlating a received signal with one or more known
patterns. In some embodiments of a TV-band cognitive radio system, the
incumbent signal can contain known signal patterns. In some embodiments
an incumbent signal can be a Digital Television (DTV) signal and/or an
NTSC signal and/or any other known and/or convenient signal organized as
a channel and/or having adjacent channels. In some embodiments specified
performance levels can be achieved through the use of phase sensing
techniques. The terms phase sensing, waveform sensing, and/or signal
sensing can be considered substantially identical as discussed herein.

DTV Signal Sensing:

[0042] A broadcast DTV signal can contain a Data Field Sync [ATSC,
"Digital television standard," ATSC Digital Television Standard, August
2001] segment of 832 symbols (at a symbol period 0.186 μs and/or a
sampling frequency of 5.38 MHz) occurring every 24.2 ms. The diagram 500
of FIG. 5 shows the format of a DTV Data Field Sync segment that contains
a 511-element PN sequence and three 63-element PN sequences, all of which
can be specified and/or known.

[0043] A PN sequence can have useful correlation properties. A PN sequence
correlated with any rotated version of itself can produce a result of -1,
except when two PN sequences are aligned, in which case the result is
equal to the PN sequence length. A PN sequence can be cyclically extended
to create an infinite sequence s(n). It follows that

in equation (7). A sensing gain over a nominal symbol SNR can then be
Nc. For example, a 63-element PN sequence can be used for sensing,
wherein a sensing gain can be calculated

10 log10(63)=18 dB (9)

[0044] In some embodiments, a sensing gain of more than 20 dB can be
achieved when a 511-element PN sequence is employed.

ATSC Signal Sensing: Signal Sensing:

[0045] The format of the Data Field Sync segment (one in each Data Field
of 313 832-symbol segments) can be as shown in diagram 500 of FIG. 5. The
Data Field Sync segment contains 4 binary-modulated pseudo-random
sequence, i.e. one PN511 and 3 PN63. These are intended for ATSC receiver
channel equalization.

[0046] When coherently combined, the PN sequences can provide significant
coding gain over a nominal symbol SNR and can be employed for some
embodiments with reliable low-threshold ATSC signal detection. For
example, a PN63 sequence can provide about 10*log 10(63)=18 dB gain over
nominal SNR and a PN511 sequence can provide about 10*log 10(511)=27 dB
gain over a nominal SNR.

[0047] Diagram 1800 of FIG. 18 depicts an embodiment of a detailed block
diagram for an ATSC signal sensing algorithm for an input signal y(n)
1801, and providing a sensing result 1838. An algorithm is based on the
use of the PN sequences PN63 and PN511 in the ATSC Data Field Sync
segment. The algorithm can be described as comprising essentially two
distinct parts, an upper part 1851 and a lower part 1852. The upper part
1851 can compute a signal auto-correlation using the 3 repeated PN63
sequences (refer to Data Field Sync segment format in FIG. 5). The
auto-correlation can generate a frequency offset estimation that can be
used to compensate the input signal so that signal cross-correlation with
known PN63 and/or PN511 sequences can be performed as shown in the lower
part 1852.

[0048] An auto-correlation operation can be expressed in the following
formula:

J ( n ) = l = n - 62 n y * ( l - 63 )
y ( l ) ( 10 ) ##EQU00010##

Here y(l) is a received signal and y(l-63) is an input signal delayed by
63 sampling cycles; it is the output of a 63-element delay line Delay 63
1802. Conjugation, multiplication, and summing operations of Equation
(10) can be performed by the respectively corresponding modules Conjugate
1804, Multiply 1806, and Running Sum 63 1808. J is the output of Running
Sum 63 1808.

[0049] An auto-correlation magnitude |J(n)| curve can be expected to have
a plateau when the input y(n) enters the PN63 region within the Data
Field Sync segment, as illustrated in FIG. 19 where |J(n)| is shown as
the thicker curve. The auto-correlation magnitude |J(n)| normalized by
the average signal power can be compared against a predefined threshold
in order to determine whether such a situation has occurred.

[0050] An average signal power can be computed by elements of diagram
1800, including a Magnitude Square 1810 module and an Averaging 1812
module. An average signal power can be expressed as:

P ( n ) = 1 N l = n - ( N - 1 ) n y (
l ) 2 ( 11 ) ##EQU00011##

A direct implementation of averaging in Equation (11) can be achieved
employing numerous techniques well known in the art. In a reduced
hardware implementation embodiment, an Infinite Impulse Response (IIR)
with a forgetting factor 1/N can be employed. The HR filter output can
serve as a close approximation to an actual running sum output. Operation
of an averaging IIR filter embodiment can be expressed as:

P ( n ) = 1 N y ( n ) 2 + ( 1 - 1 N )
P ( n - 1 ) ( 12 ) ##EQU00012##

where P(n-1) is the IIR filter output for the sampling cycle immediately
previous. It can be appreciated that averaging depth N is a parameter
common to a plurality of implementations. In some embodiments, a
relatively large N can be employed in order to achieve specified
performance criteria for computation of average power. For example, N can
be chosen to be 500.

against a specified pre-defined threshold, e.g. 0.45. A threshold can be
specified such that values exceeding the threshold indicate a specified
reasonable confidence of entering a PN63 region of a Data Field Sync
segment, whereupon a peak search operation can follow.

[0052] A Peak Search 1822 operation can be controlled by two time stamps:
peak search start p0 1816 and peak search end p1 1818. These time stamps
can be generated by a Control Timer 1820 module. In the event that an
autocorrelation threshold is exceeded at sampling index n, a requirement
can follow that:

p0≧n (14)

A p0 1816 value can typically be chosen to be equal to n. The duration
between time stamps:

TPS=p1-p0 (15)

can be specified to be long enough so that it can cover a plateau region
of normalized auto-correlation magnitude as shown in diagram 1900. For
example, TPS can be chosen to be twice the PN63 length, i.e. 126.

[0053] Between the two time stamps p0 and p1, a Peak Search 1822 module
can be activated in order to search for an auto-correlation J(n) that
corresponds to a maximum normalized auto-correlation magnitude, as
expressed in Equation (13). This maximum auto-correlation can be used to
estimate a frequency offset and/or to generate a phase increment in order
to derotate an input signal in order to compensate for a frequency
offset. Taking into account frequency offset, the auto-correlation can be
expressed as:

with per sample frequency offset ρ=ΔfcTs where
Δfc is the frequency offset and Ts is the sampling
period. Referring to diagram 1900, for a maximum J(n) (within the plateau
region) it can be expected that

y(l-63)=y(l) (17)

if noise is neglected. Thus Equation (16) becomes:

j2πρ 63 = J ( n ) l = n - 62 n y
( l ) 2 ( 18 ) ##EQU00015##

and a frequency offset per sample can be estimated:

2 πρ = arg [ J ( n ) ] 63 ( 19 )
##EQU00016##

The above computation can be performed by the Phase Incre. Comp. 1824
module. The module can generate an incrementing phase factor:

e-j2πρn (20)

that can be used to compensate input signal samples y(n) 1801 for
frequency offset when (complex) multiplied by this factor. The complex
multiplier 1826 can perform this multiplication on a delayed version of
the input signal.

[0054] ATSC signal sensing can be performed by correlating compensated
input signal samples with known PN sequence patterns. PN511 and/or PN63
can be used for such correlation. In order for correlation to be
performed after frequency offset is computed, a start time of such
correlation c0 can be required to satisfy:

c0≧p1 (21)

[0055] In some embodiments a delay element Delay X 1828 can be provided
between input signal samples and complex multiplier 1826 employed for
frequency offset compensation. This delay element can ensure that
frequency offset compensation will not be missed on the target PN
sequence pattern.

[0056] In one example, the desired PN sequence pattern can be PN511,
referring to diagram 1900. Assuming that the start of PN511 is n0, a
length X of the delay element Delay X 1828 can be required to satisfy:

X≧c0-n0≧p1-n0=TPS+(p0-n0) (22)

Assuming that p0 occurs before the end of a third PN63, then a maximum
distance between p0 and n0 can be 511+63×3=700. The length of the
delay element can be required to be at least:

X=TPS+700 (23)

If the desired PN sequence is the third PN63, a maximum distance between
p0 and n0 is 63, and thus a required length of delay element can be:

X=TPS+63 (24)

[0057] Note that as discussed previously, PN511 can give higher coding
gain than PN63 (27 dB versus 18 dB). However, if a desired coding gain is
less than 18 dB, the third PN63 can be selected as the target correlation
pattern in order to minimize computational complexity.

[0058] Compensated signal samples can be piped through a shift register
Shift Register Y 1830 whose size can be equal to the length of a target
PN sequence, e.g. 511 for PN511 and 63 for PN63. A signal pattern in the
shift register can then be correlated with a target PN sequence to
generate a sensing metric for thresholding, i.e.

M ( n ) = l = 0 Y - 1 s ( n - l ) b
( Y - 1 - l ) ( 25 ) ##EQU00017##

Here s(n-l) is the l-th element of the shift register and b(l) is the
l-th element of the PN sequence. Y can be take on the value of 511 or 63,
depending on the PN sequence used. Equation (25) can be implemented by
modules Shift Register Y 1830, vector multiplier 1832, PN_Y 1834, and Sum
Y 1836. PN_Y 1834 can provide a PN sequence to the vector multiplier
1832. Sum Y 1836 can perform a summing operation on input provided by the
vector multiplier 1832 and provide a result to a Sensing Threshold 1840
module.

[0059] Power of an averaged sensing metric normalized by an average signal
power, i.e.:

1 Y M ( n ) 2 P ( n ) ( 26 )
##EQU00018##

can then be compared with a threshold to determined ATSC signal presence.
For example, such a threshold could have a value of 0.25. Threshold
comparisons can be performed by the Sensing Threshold 1840 module.

[0060] Sensing Threshold 1840 module can be activated between times
represented by time stamps s0 1842 and s1 1844; these time stamps can be
generated by a Control Timer 1820 module. For example, s0 can be chosen
to be p1, and, s1 can be chosen to ensure that a desired PN sequence
completely passes through the shift register.

NTSC Signal Sensing:

[0061] A NTSC (analog TV) signal can contain narrowband video, chroma, and
audio carriers. Video, chroma, and audio carriers can be located at 1.25
MHz, 4.83 MHz, 5.75 MHz from the left band edge respectively, as depicted
in the diagram 600 of FIG. 6.

Since a video carrier can have significantly higher power than other
carriers in the composite signal, some embodiments of NTSC signal sensing
can be based on video carrier sensing; hence chroma and audio carriers
are neglected in the following analysis. A transmitted TV signal can be
approximated in the following form:

It can be appreciated that such a sensing operator can be essentially
equivalent to an FFT-based narrowband filter. A multipath delay spread
can be assumed to be time-limited; it follows that c(p) can be nonzero
only when

[0064] Note that in some embodiments a NTSC data signal can be considered
as noise when sensing is performed, since it is not being decoded. Since
a data signal and noise are independent, a total sensing metric noise
power can be expressed as:

[0065] In some embodiments, a sensing gain can decrease with an increasing
SNR. In some embodiments this property can be neglected since at high
SNR, sensing can be relatively successful essentially regardless of a
sensing gain value. A factor η can be a measure of multipath fading
on a video carrier. When the fading is significant, the sensing gain can
become smaller. The graph 700 of FIG. 7 shows sensing gain contours at
different SNRs and fading levels η where N=256 and μ=0.875 (TV
video signal modulation index according to [A. B. Carlson, op. cit.]). It
can be appreciated that sensing gain can increase with increasing N. In
some fading environments, a narrowband fade could be larger than 30 dB
[T. S. Rappaport, Wireless Communications Principles and Practice.
Prentice-Hall, 1996]. Rather than simply increasing the correlation depth
N in order to ameliorate this effect, it can be advantageous in some
embodiments to share sensing information with other users. In some
embodiments, multipath fading can be a local phenomenon whose effects can
be statistically reduced by combining sensing results from different
locations.

Energy Sensing:

[0066] Energy sensing can be employed as a fundamental method of signal
detection in some embodiments; any information-conveying signal has
finite energy. In some embodiments, energy-based sensing can suffer from
a drawback of a longer convergence time as compared to phase-based
sensing, as discussed herein regards DTV and NTSC signal sensing.
Energy-based sensing can achieve salutary specified performance levels
under the conditions of a high SNR, in some embodiments. Furthermore, in
some embodiments energy-based sensing can be used to estimate a SNR. A
received signal y(n) can be expressed as

y(n)=x(n)+z(n) (49)

A sequence of signal samples x(n) can be assumed to be independent, in
order to simplify derivation. Correlation between signal samples x(n)s,
e.g. due to multipath channel memory effect, can (only) improve sensing
performance. Since the noise sample z(n)s are independent, the received
signal sample y(n)s are independent. Consider a signal energy S obtained
by averaging:

S = 1 N B n = 1 N B y ( n ) 2
( 50 ) ##EQU00034##

where NB can be an averaging buffer size. Since |y(n)|2 can be
a sequence of independent and identically distributed (IID) random
variables with mean and variance, it follows that:

E[|y(n)|2]=μ (51)

E[|y(n)|4]=σ2 (52)

Assuming NB>>1, according to the central limit theorem, S can
be approximated as a Gaussian random variable with mean μS and
variance σS2:

μ S = μ ( 53 ) σ S 2 = σ 2 N B
( 54 ) ##EQU00035##

PFD vs. PLD: When there is no signal present, i.e. x(n)=0, the result of
averaging can be expressed:

S 0 = 1 N B n = 1 N B z ( n ) 2
( 55 ) ##EQU00036##

When there is a signal present, the result of the averaging can be
expressed

S 1 = 1 N B n = 1 N B y ( n ) 2
( 56 ) ##EQU00037##

where y(n) is given by Equation (49). A threshold ST can be used to
determine whether a signal is present. A value of (averaged) signal
energy exceeding the threshold can correspond to the presence of a
signal. A value of (averaged) signal energy not exceeding the threshold
can correspond to the absence of a signal.

[0067] As illustrated in the graph 800 of FIG. 8, a probability of false
detection (PFD) is the probability that the sensing indicates signal
presence (i.e. energy average of Equation (50) above threshold) when
there is noise but essentially no signal present. A probability of loss
detection (PLD) is the probability that sensing indicates absence of a
signal (i.e. energy average Equation (50) below threshold) when there is
a signal present. A threshold can be set relatively high, thus reducing
PFD at the expense of increased PLD. The threshold can be set relatively
low, thus reducing PLD at the expense of increased PFD. Thus an optimal
threshold ST can be selected at an operating point where PFD is
essentially equal to PLD.

Sensing Error Floor Calculation:

[0068] A sensing error floor (SEF) can be defined as the PFD, or
equivalently PLD, at an optimal threshold. A general expression for a
sensing error floor can be derived. Referring to FIG. 8, a PFD can be
expressed using a Q function:

using a symmetry property of the Q function. (A Q-function can be defined
as the complement of a standard normal cumulative distribution function.)
At the sensing error floor, the PFD is equal to the PLD, i.e.

Assuming E[x]=E[z]=0 all terms involving the first-order of x and z in
equation (73) can vanish after an expectation operation. Since z is a
complex Gaussian random variable, i.e. z=u+jv, it follows that

E[z2]=E[u2-v2]+2jE[u]E[v]=0=E[(z*)2] (74)

and thus all terms involving the second-order of z in equation (73) can
vanish after expectation. Equation (73) can then be simplified to:

E[|y|4]=E[|x|4]+E[|z|4]+4E[|x|2]4E[|z|2] (75)

For a complex Gaussian random variable z,

E[|z|4]=2{E[|z|2]}2=2Pn2 (76)

For

E[|x|4]=α{E[|x|2]}2=αPS2 (77)

Equation (75) can be expressed as

E[|y|4]=αPS2+2PN2+4PNPS (78)

Substituting the above in equation (72) and using equation (71), it
follows that

[0069] The graph 900 of FIG. 9 shows the sensing error floor contours at
various SNRs and averaging buffer sizes. Note that the error floor is
expressed in dB units, e.g. -20 dB corresponding to an SEF of 0.01. When
SNR<<1, the argument of the Q function in equation (81) can be
approximated as

N B SNR 2 ( 82 ) ##EQU00050##

Thus, in some embodiments a linear decrease in SNR can be met by a
quadratic increase in buffer size NB in order to maintain a
specified SEF value. When SNR>>1, the argument of the Q function in
equation (81) can be approximated as {square root over (NB)}
independent of SNR.

Energy-Based Sensing:

[0070] Energy-based sensing is a generic sensing method that can apply to
many classes of signals, since any information-bearing signals have
essentially nonzero energy. Energy-based sensing can work well when a
noise signal is far smaller in energy than a target signal. However, in
some embodiments, the reliability of energy-based sensing can be
significantly impaired when the target signal power approaches the noise
floor. In addition, since energy sensing does not differentiate signal
types, energy-based sensing can be susceptible to false detection, e.g.
due to impulse noise and/or other types of radio activity.

[0071] In an embodiment of energy-based sensing, average signal power
within a specified target channel can be computed. Signal power can then
be compared to a noise floor and can be thresholded to determine signal
presence. The computation can also lead to a channel SNR estimation that
can be useful elsewhere.

[0072] A block diagram 2100 for some embodiments of energy sensing is
depicted in FIG. 21. The upper branch of the graph illustrates
computation of signal power in a target channel. A filter block 2102 can
essentially provide a significant information-bearing portion of signal
spectrum while removing an unreliable portion of the signal spectrum
(e.g. channel edges which can be contaminated by signal leakages from
adjacent channels). For a signal after the filter y(n), a target channel
signal power can be computed as:

[0073] A key part of an energy sensing algorithm can be computation of
noise power. A target signal power can be compared against a computed
noise power. For a TV signal, noise power can be computed using a
reference channel that contains essentially no signal transmission. In
the TV band, Channel 37 is typically reserved for astronomy observations;
typically essentially no signal transmission occurs. This channel can be
used as a reference channel. Signal power computation on the reference
channel can be essentially the same as that for the target channel, as
shown in the lower branch of the graph. The reference channel power can
be computed as:

P 0 ( n ) = 1 N l = n - ( N - 1 ) n y
0 ( l ) 2 ( 84 ) ##EQU00052##

where y0(n) is an input signal on the reference channel after
filtering. A sensing decision can be made by comparing target channel
signal power P(n) against the reference channel signal power P0(n)
using a specified threshold. A signal-to-noise ratio can be computed as:

SNR ( n ) = P ( n ) - P 0 ( n ) P 0 ( n
) ( 85 ) ##EQU00053##

Threshold or Div. 2108 can provide a sensing result by comparing and/or
thresholding the averaged power of a target channel signal (provided by
Averaging 2106) with an averaged power of a reference channel. A signal
processing chain comprising Filter 2112, Magnitude Square 2114, and
Averaging 2116 can provide a static and/or a dynamic averaged power for a
reference channel. A dynamic averaged power can also be otherwise
specified and/or computed as discussed herein. Filter 2112 can provide
filtering for a reference channel as Filter 2102 does for a target
channel. Magnitude Square 2114 and Averaging 2116 modules can provide
power computation and averaging to the reference channel signal path as
are provided to the target channel signal path by the corresponding
modules 2104 2106. In some embodiments Threshold or Div. 2108 module can
provide a SNR result as specified in Equation (85).

System and Network Layer Analysis:

[0074] Some important system and network layer techniques are herein
described that can be used in order to facilitate TV-band cognitive radio
operation. For example, since TV receivers are typically passive,
reactive interference control by detecting their presence is essentially
not possible in some embodiments. Instead, a proactive approach can be
taken through collective sensing by networked cognitive users in order to
reduce interference in a statistical sense. To maximize the potential of
cognitive user transmission while limiting adjacent channel interference
to TV users, a cognitive user transmission power can be controlled
adaptively based on signal power measurements of adjacent TV channels. A
hidden terminal probability analysis can quantify a TV-band hidden
terminal probability, and is described herein. Collective sensing can
help reduce hidden terminal probability, and is described herein. Rate
and range adaptation to optimize system throughput under various field
conditions is described herein. Also herein described are embodiments of
collective sensing and adaptive transmission power control in a TV-band
cognitive radio system.

Hidden Terminal Probability Analysis

[0075] Instances of a hidden terminal can pose a significant challenge to
some embodiments of a TV-band cognitive system. In a TV-band cognitive
radio system, TV stations and TV receivers can be primary users. Since TV
receivers typically do not transmit, detecting the presence of a TV
receiver is not straightforward. The hidden terminal problem in a TV-band
cognitive system can alternatively be addressed in a statistical fashion.
First, the hidden terminal problem can be quantified in terms of a hidden
terminal probability (HTP). A mechanism is herein described to then
reduce hidden terminal probability through sensing information sharing
amongst cognitive users; this approach is called collective sensing.

[0076] A uniform propagation loss model is described herein, wherein
signal propagation loss can increase monotonically with distance. A case
of shadowing is herein described, where propagation loss can depend on
propagation distance and/or environmental attenuation.

HTP With No Shadowing:

[0077] A signal transmission from a primary user U1 1002 to another
primary user U2 1004 in a propagation environment with uniform
propagation loss is depicted in the diagram 1000 of FIG. 10. d0 1006
can represent a maximum distance within which a primary user can detect
another primary user. d1 1008 can be a maximum distance within which
a cognitive user can detect a primary user. d2 1010 can be a maximum
distance within which a transmission from a cognitive user can be
considered harmful to a primary user.

[0078] As depicted in the illustration, for a signal transmission from
U1 to U2, a cognitive user who appears in the marked hidden
terminal region A1 1012 cannot detect U1 and can create harmful
interference to U2 upon choosing to transmit. A worst case
interference, i.e. the largest A1, can result when U2 is at a
maximum distance d0 from U1.

[0079] Assuming a cognitive user area density of ρ, the hidden
terminal probability--the probability that at least one cognitive user
appearing in the interference region A1, can be expressed

PHT=1-e-ρA1 (86)

as derived herein. Referring to FIG. 10, in order to completely eliminate
the interference can require that

d1≧d0+d2 (87)

in which case A1=0 and PHT=0. In a log-distance path loss model
[T. S. Rappaport, op. cit.], loss from a transmitter to a receiver at a
distance r away can be expressed as

L _ ( r ) = K ( r 0 ) ( r r 0 ) α
( 88 ) ##EQU00054##

Here α is the path loss exponent characteristic of the propagation
environment; r0 is the distance from the transmitter to a close-in
reference point; and K (r0) is the loss from the transmitter to the
reference point. For simplicity, the primary user and cognitive user can
be assumed to have essentially the same r0 and K(r0). It can be
appreciated that such assumptions can effectively be equivalent to
assuming that a TV station transmitter has the same elevation as a
cognitive user transmitter. Under these assumptions, a TV signal path
loss can be significantly overestimated.

[0080] As illustrated in the diagram 1100 of FIG. 11, primary user
transmission power is P0 1102; cognitive user transmission power is
P1 1104; a primary user decoding threshold power is PD 1106; a
cognitive user sensing power can be PS 1108; and a harmful
interference threshold power level can be PH 1110. Since losses for
the distances d0 d1 d2 (figure elements 1112 1114 1116, respectively) can
be expressed:

[0082] FIG. 12 illustrates an embodiment in which a typical TV station
transmission power is approximately 100 kW and a cognitive user's maximum
transmission power is approximately 1 W (P0/P1=50 dB). In some
embodiments an 8 dB PD/PS can ensure essentially
interference-free operation under the conditions of typical path loss
exponents.

HTP in Shadowing Environment:

[0083] A typical propagation environment can be non-uniform. Receivers
disposed at essentially equivalent distances from a particular
transmitter can simultaneously experience different received signal
strengths from that transmitter. Variations can depend on signal paths
between transmitter and receivers. This effect is called shadowing. The
log-distance path loss of Equation (88) can be expressed in dB form

LdB(r)=KdB(r0)-10α log10r0+10α
log10r (93)

and an actual loss at a particular receiver can be modeled as

LdB(r)= LdB(r)+X.sub.σdB (94)

where LdB(r) is considered as an average path loss while a zero-mean
Gaussian random variable X.sub.σdB with standard deviation a
can account for the effect of shadowing [T. S. Rappaport, op. cit.].

[0084] A signal transmission from primary user U1 1002 to primary
user U2 1004 is depicted in the diagram 1300 of FIG. 13. Given a
distance between U1 and U2 of r, a distance between a
potentially interfering cognitive user and U2 of Δ, and a
distance between U1 and the cognitive user of r', it can follow that
due to the transmission power difference between U1 and the
cognitive user, r>>Δ and

L(r)≈ L(r+Δ)≧ L(r')≧ L(r) L(r')≈
L(r) (95)

[0085] In some embodiments a potentially interfering cognitive user can
experience essentially the same path loss for a signal coming from
U1 as for a signal coming from U2. In another case, U2 can
be at a cell edge, i.e. r=d0. Signal path loss from U1 to a
cognitive user can then be expressed

LdB(d0)+X.sub.σdB (96)

Successful sensing of the signal by the cognitive user can require

P0dB-[
LdB(d0)+X.sub.σdB]>PSdBX.sub.σ.su-
p.dB<PDdB-PSdB

with a sensing success probability of

Γ = 1 2 π σ ∫ - ∞ P D
d B - P S d B - x 2 2 σ 2
x ( 97 ) ##EQU00056##

(PD and PS are in dB units and Γ is a probability value
between 0 and 1.) Because of shadowing, the interference region can
extend beyond d2 (from U2), with interference probability
decreasing with increasing distance from U2. An effective
interference region A1 1302 around U2 can be assumed (for
simplicity):

A1=πd12 (98)

noting that d1 1304 can be on the same order as d2 for this
assumption. A cognitive user that is located in effective interference
region A1 and that does not successfully sense a signal from U1
can be identified as a hidden terminal.

as derived herein. For a resulting constant
A1=πd12˜πd22, an essentially zero
HTP thus corresponds to Γ=1. Such a constraint could require
essentially infinite sensing gain PD/PS to meet a specified
performance criterion. For a finite PD/Ps, HTP in Equation (99)
is monotonic with cognitive user density ρ. As ρ increases, HTP
can exceed a tolerable specified performance level. HTP with Sensing
Information Sharing:

[0087] In some embodiments the above dilemma can result from sensing being
performed by a lone local cognitive user. If a user happens to experience
severe shadowing, the user may not be able to detect a primary user
signal even with relatively large sensing gain. As cognitive user density
increases, such a situation can become more likely and can result in
increased interference to a primary user.

[0088] This system behavior can be addressed in some embodiments by
employing sensing information sharing between cognitive users. As an
increasing number of cognitive users share their sensing results, the
probability of shadowing can be reduced exponentially, and hidden
terminal probability can be similarly reduced. This approach can be
called collective sensing by a cognitive user network.

Referring to FIG. 13, all cognitive users in an area AC 1306 around
U2 and defined by radius dC 1308,

AC=πdC2 (100)

can share their sensing information so that a cognitive user located in
A1 1302 transmits only under the condition that essentially none of
the cognitive users in AC 1306 sense a signal from U1 1002.
Assuming AC≧A1, a hidden terminal probability can be
expressed

PHT=e-ρAC.sup.Γ[1-e-ρA1.sup.(1-.G-
AMMA.)] (101)

as derived herein.

[0089] Comparing Equation (101) with Equation (99), the factor
e-ρAC.sup.Γ--a result of sensing information
sharing--can help to drive down HTP as ρ increases. This effect is
shown in the graph 1400 of FIG. 14 under the assumption of
A1=AC. Γ can be calculated using Equation (97); a
shadowing environment can be characterized by using a random Gaussian
variable Xf with σ=11.8 dB based on an urban cellular measurement
[T. S. Rappaport, op. cit.].

[0091] Graph 1400 shows that an arbitrarily low HTP can be achieved in
some embodiments by enlisting an adequate number of cognitive users
ρAC in sharing their sensing information. A target HTP can be
achieved by requiring a threshold number of cognitive user participants.
This threshold can be called the critical mass of collective sensing. The
critical mass MC of collective sensing, cognitive user participants
can be defined as

MC=ρAC (102)

such that Equation (101) achieves a predefined HTP. Graph 1500 of FIG. 15
depicts the MCs required to achieve an HTP of 10-4 in a
shadowing environment with σ=11.8 dB for varying values of sensing
gain PD/PS and area ratio AC/A1. For example, when
AC/A1=2 and PD/PS=10 dB, a critical mass of 11
cognitive users can achieve a target HTP of 10-4

Rate Coverage Area:

[0092] Supporting system rates as discussed above can require different
SNRs. A coverage area (i.e. cell size) for a particular data rate can be
defined as an area around a transmitter where a received signal SNR is
above a required SNR level corresponding to the data rate.

[0093] A log-normal path loss model of Equation (88) can be used to
calculate a coverage area. A cognitive transmitter can be assumed to have
a clearance of 10 meters (noting the same clearance is used in the
TV-band per the NPRM [FCC, May 2004, op. cit.]. Within the clearance
distance, signal propagation can follow a free-space propagation model.
Signal power at the edge of a clearance region can be calculated as

P ( r 0 ) = ( c 4 π fr 0 ) 2 P TX
( 103 ) ##EQU00057##

which is also known as the Friis free-space equation [T. S. Rappaport,
op. cit.], and where c is the speed of light. Signal power loss from a
transmitter to an edge of the clearance region can be expressed

For example, K (r0) is 48 dB if r0=10 m and f=600 MHz. Signal
propagation beyond the clearance region can follow a log-normal path loss
model as shown in Equation (88), and signal power at a distance r away
from the transmitter can be expressed

PdB(r)=PTXdB-KdB(r0)-α(rdB-r0.s-
up.dB) (105)

noting the distance is expressed in dB units as well. Successful
reception can be achieved for signal power exceeding a threshold VT.
The coverage area radius can be expressed

and where N0=-106 dBm is the thermal noise in a 6 MHz TV channel; NF
is the receiver noise figure; and SNRT is the SNR threshold for
successful decoding. SNR thresholds for various data rates are described
previously.

[0094] Graph 1600 of FIG. 16 shows signal coverage areas at different data
rates assuming a carrier frequency of 600 MHz, a clearance radius of 10
m, transmission power of 30 dBm (1 W), a path loss exponent α=3.3,
and a receiver noise figure of 8 dB. It can be appreciated that these
coverage areas can be derived based on additive white gaussian channel
(AWGN) SNRs and that areas can shrink if multipath margin is included in
the SNRs. Notably, relative coverage area sizes can be independent of
transmission power.

Collective Sensing:

[0095] Collective sensing can be key to reducing hidden terminal problems
in a TV-band cognitive system as discussed herein. Collective sensing can
be employed in some embodiments as described herein following.

[0096] Essentially all cognitive users considered in an embodiment can be
required to periodically, e.g. every 10 seconds, broadcast their sensing
results. A broadcast message from each cognitive user can include its SNR
estimates and/or DTV and/or NTSC signal sensing outcomes on all TV
channels. In some embodiments, such a broadcast message can be
transmitted using a lowest data rate that has a largest coverage area, as
shown in FIG. 16.

[0097] Each cognitive user in an embodiment can also "listen" to (receive
and respond to) messages from potentially all of the other cognitive
users in the embodiment. In order to transmit and/or otherwise use a TV
channel, a cognitive user must collect negative sensing results on the
target TV channel from at least MC cognitive users where MC is
a critical mass corresponding to a specified level of hidden terminal
probability as discussed regards Equation (102). For example, suppose a
harmful interference level PH (as shown in FIG. 11) is at the noise
floor. Since sensing results can be shared using the lowest data rate
with a required SNR of -11 dB, an area ratio between the sensing result
sharing region and potential interference region can be expressed as
approximately

ACdB-A1dB-2(rCdB-rIdB)≈2-
10/3.3.6 dB (107)

corresponding to AC/A1=4 in linear scale. Using sensing methods
as discussed herein, a sensing gain PD/PS of 15 dB can be
achieved. Referring to FIG. 15, a required critical mass for 10-4
HTP is then 9.

ATSC Signal Sensing: ATSC Frame Format:

[0098] An ATSC data transmission can be organized into Data Frames as
shown in diagram 1700 of FIG. 17. Each Data Frame contains two Data
Fields, each containing 313 Data Segments. The first Data Segment of each
Data Field is a unique training sequence that can typically be used for
channel equalization in a receiver.

[0099] The remaining 312 Data Segments carry data. Each Data Segment
encodes a 188-byte transport stream packet and its associated 20 Reed
Solomon FEC bytes. Each data segment consists of 832 8-VSB symbols and
each symbol is a 8-level signal carrying 3 bits of information. The first
4 symbols of the Data Segment corresponding to the first (sync) byte of
the 188-byte transport stream are transmitted in binary form and provide
segment synchronization. The remaining 828 symbols can carry a total of
828*3=2484 bits of information. Since the symbols are 2/3 trellis coded,
the equivalent number of data bits carried in the 828 symbols can be
2484*2/3=1656 or 207 data bytes, e.g. 187 transport stream bytes plus 20
RS FEC bytes. Notably, the first byte of a 188-byte transport packet is
transmitted in the first 4 symbols of a Data Segment for segment sync.

NTSC Signal Sensing:

[0100] An NTSC (analog TV) signal can contain narrowband video, chroma,
and audio carriers. These carriers can be at 1.25 MHz, 4.83 MHz, and 5.75
MHz from the left band edge, respectively. These relationships are
illustrated in diagram 600 of FIG. 6. NTSC signal sensing can be based on
detecting narrowband NTSC video and audio carriers. An FFT-based
narrowband filter scheme can be used in order to detect video and audio
carriers, as illustrated in diagram 2000 of FIG. 20.

[0101] A FFT size can be selected so as to be large enough to enable
resolution of video and audio carriers. For example, for a 6 MHz TV
channel, an FFT size of 256 or bigger can be used. As shown in diagram
2000, an input signal spectrum can be obtained by transforming a
time-domain input signal to frequency domain using FFT. Bins around a
video carrier can be extracted, and bins around an audio carrier can be
extracted. Module FFT 2002 can perform an FFT operation on an input
signal.

[0102] The number of bins taken for detecting the video and/or audio
carriers can be specified, and can depend on channel bandwidth and FFT
size. The number of bins taken can be specified to be large enough so
that the probability of missing a target carrier is minimized. A missing
carrier can occur if there is frequency offset between transmitter and
receiver. For example, for a system that uses a 1024-point FFT over a 6
MHz channel bandwidth, the subcarrier spacing can be 5.86 kHz. It follows
that in some embodiments of detection, 10 bins each around the video and
audio carriers can be specified. Modules 2010 2020 2030 can be employed
to extract video carrier, midband, and audio carrier bins, respectively.

[0103] In order to detect either video or audio carriers, power on each
extracted bin can be evaluated, and an average over the bins taken.
Magnitude Square modules 2012 2022 2032 can provide a
magnitude-squared-based power measurement for video carrier, midband, and
audio carrier signals, respectively. The power measurements can be
bin-averaged by Averaging (over bins) modules 2014 2024 2034 (again
respectively). The resulting bin-averaged power(s) can each be averaged
again over a predefined number of symbols, e.g. 10, in order to improve
sensing reliability. The bin-averaged power measurements can be
symbol-averaged by Averaging (over symbols) modules 2016 2026 2036 (again
respectively). Averaging processes can use implementations discussed
herein and/or any other known and/or convenient averaging technique. In
some embodiments, for averaging over a small number of elements, a
running sum approach can be advantageously employed.

[0104] Averaged video and audio carrier powers can be appropriately
normalized before thresholding for detection. An average power over bins
in the center of a TV channel can be used as a basis for normalization,
based on FIG. 6. According to FIG. 6, bins in the center of a TV channel
can be relatively free of narrowband carriers, and, their average power
can be a relatively accurate reflection of average power over the whole
channel. Video and audio carrier powers can be expected to exceed average
power over mid-band bins by a margin. The margin value can, by way of
non-limiting example, be 10 dB. Further, comparing normalized video and
audio carriers with such a threshold can enable a determination of the
presence of a NTSC signal. If the video and audio carriers each exceed
such a threshold, a positive NTSC signal detection result can apply. If
the carriers do not exceed a threshold, a negative NTSC signal detection
result can apply. A threshold 2004 module can perform a comparison
between video carrier power and midband power, providing a NTSC signal
detection result to an AND 2008 module. A threshold 2006 module can
perform a comparison between audio carrier power and midband power,
providing a NTSC signal detection result to an AND 2008 module. An AND
2008 module can be employed to logically combine the result of signal
detection results from threshold 2004 and threshold 2006 in order to
provide a combined NTSC signal detection result. An AND 2008 module can
perform specified logic and/or other operations (such as time-based
operations) on provided input results in order to provide one or more
combined results.

[0106] A cognitive radio unit CR 2201 can comprise a phase sensing element
2204, an energy sensing element 2205, and a collective sensing element
2206. Phase sensing element 2204 and energy sensing element 2205 can be
adapted to provide sensing functions as discussed herein, employing phase
sensing techniques and energy sensing techniques, respectively. Phase
sensing element 2204 and energy sensing element 2205 can be adapted to
communicate and/or cooperate with each other, that is, interoperate,
according to a specified physical layer 2203. A cognitive radio unit CR
2201 can further comprise a collective sensing element 2206. A collective
sensing element 2206 can interoperate with other sensing elements
according to a specified network and protocol layer 2202. As depicted in
Diagram 2200, phase sensing 2204 and/or energy sensing 2205 elements can
also interoperate with other sensing elements according to a specified
network and protocol layer 2202.

[0107] As illustrated in Diagram 2200, a second cognitive radio unit CR
2211 can be substantially similar in architecture to CR 2201. CR 2211 can
comprise a phase sensing element 2214, an energy sensing element 2215,
and a collective sensing element 2216. Phase sensing element 2214 and
energy sensing element 2215 can be adapted to interoperate according to a
specified physical layer 2213. A collective sensing element 2216 can
interoperate with other sensing elements according to a specified network
and protocol layer 2212. Phase sensing 2214 and/or energy sensing 2215
elements can also interoperate with other sensing elements according to a
specified network and protocol layer 2212.

[0108] Sensing elements in a first cognitive radio unit CR1 2201 can
interoperate with sensing elements in a second cognitive radio unit CR2
2211 according to one or more specified network and protocol layers. In
some embodiments, a specified network and protocol layer 2202 can be the
same network and protocol layer 2212.

[0110] Sensing decisions for individual and/or multiple cognitive radio
units 2201 2211 can be determined collectively through combining physical
layer sensing results from a plurality of nearby cognitive radio units.
In some embodiments, a collective sensing result can be determined as a
weighted average of physical layer sensing results from nearby cognitive
radio units. A weighting for a physical layer sensing result can be based
on a specified and/or measured distance between a cognitive radio unit
and another cognitive radio unit and/or a specified location; a specified
location can be the location of a physical layer sensing process. That
is, in some embodiments a weighting can be responsive to measured and/or
specified locations and/or distances: between cognitive radio units,
between a cognitive radio unit and a specified location, and/or between
specified locations. In some embodiments, a distance metric can be based
on a measurement of radio signal strength detected from a specified
cognitive radio unit.

[0111] By way of non-limiting examples, communications for collective
sensing and between two or more cognitive radio units 2201 2211 can be
achieved through one or more of a specified cognitive radio channel, a
cellular link, a WiFi link, an Ethernet link, and/or any other known
and/or convenient wired and/or wireless communication systems. By way of
non-limiting examples, such communications can be adapted for a wide-area
network (WAN) and/or any other known and/or convenient technology for
network communications.

Hidden Terminal Probability Derivations:

Hidden Terminal Probability:

[0112] A large number N.sub.∞ of cognitive users can be assumed to
be randomly distributed over a large area A.sub.∞, covering the
area of interest A. The probability of no user appearing in A, i.e. all
users appearing outside A, can be expressed:

[0116] For any k users appearing in A1, interference can occur when
all k users are not sensing a signal from U1, in combination with
all users appearing in the area AC-A1 not sensing a signal from
U1. The total HTP can be expressed

[0117] In the foregoing specification, the embodiments have been described
with reference to specific elements thereof. It will, however, be evident
that various modifications and changes may be made thereto without
departing from the broader spirit and scope of the embodiments. For
example, the reader is to understand that the specific ordering and
combination of process actions shown in the process flow diagrams
described herein is merely illustrative, and that using different or
additional process actions, or a different combination or ordering of
process actions can be used to enact the embodiments. For example,
specific reference to NTSC and/or ATSC and/or DTV embodiments are
provided by way of non-limiting examples. Systems and methods herein
described can be applicable to any other known and/or convenient
channel-based communication embodiments; these can comprise single and/or
multiple carriers per channel and can comprise a variety of specified
channel bandwidths. The specification and drawings are, accordingly, to
be regarded in an illustrative rather than restrictive sense.

Patent applications by Haiyun Tang, Saratoga, CA US

Patent applications in class Having measuring, testing, or monitoring of system or part

Patent applications in all subclasses Having measuring, testing, or monitoring of system or part